Facial expression feature extraction method based on improved LBP

Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global features extracted.To solve these problems,a facial expression feature extraction method is proposed based on improved LBP.Firstly,LBP is converted into double local binary pattern(DLBP).Then by combining Taylor expansion(TE)with DLBP,DLBP-TE algorithm is obtained.Finally,the DLBP-TE algorithm combined with extreme learning machine(ELM)is applied in seven kinds of ficial expression images and the corresponding experiments are carried out in Japanese adult female facial expression(JAFFE)database.The results show that the proposed method can significantly improve facial expression recognition rate.

Gate Feature Interaction Network for Relation Prediction in Knowledge Graph

Recently,many knowledge graph embedding models for knowledge graph completion have been proposed,ranging from the initial translation-based model such as TransE to recent CNN-based models such as ConvE.These models fill in the missing relations between entities by focusing on capturing the representation features to further complete the existing knowledge graph(KG).However,the above KG-based relation prediction research ignores the interaction information among entities in KG.To solve this problem,this work proposes a novel model called Gate Feature Interaction Network(GFINet)with a weighted loss function that takes the benefit of interaction information and deep expressive features together.Specifically,the proposed GFINet consists of a gate convolution block and an interaction attention module,corresponding to catching deep expressive features and interaction information based on these valid features respectively.Our method establishes state-of-the-art experimental results on the standard datasets for knowledge graph completion.In addition,we make ablation experiments to verify the effectiveness of the gate convolution block and the interaction attention module.

Matching suitable feature construction for SAR images based on evolutionary synthesis strategy

In the paper,a set of algorithms to construct synthetic aperture radar（SAR）matching suitable features are frstly proposed based on the evolutionary synthesis strategy.During the process,on the one hand,the indexes of primary matching suitable features（PMSFs）are designed based on the characteristics of image texture,SAR imaging and SAR matching algorithm,which is a process involving expertise;on the other hand,by designing a synthesized operation expression tree based on PMSFs,a much more flexible expression form of synthesized features is built,which greatly expands the construction space.Then,the genetic algorithm-based optimized searching process is employed to search the synthesized matching suitable feature（SMSF）with the highest effciency,largely improving the optimized searching effciency.In addition,the experimental results of the airborne synthetic aperture radar ortho-images of C-band and P-band show that the SMSFs gained via the algorithms can reflect the matching suitability of SAR images accurately and the matching probabilities of selected matching suitable areas of ortho-images could reach 99±0.5%.

Real-time monitoring of brake shoe keys in freight cars

Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a principal means of condition monitoring. The brake shoe key （BSK） is an important component in the brake system, and its absence will lead to serious accidents. This paper presents a novel method for automated visual inspection of the BSK condition in freight cars. BSK images are first acquired by hardware devices. The subsequent in- spection process is divided into three stages： first, the region-of-interest （ROI） is segmented from the source image by an im- proved spatial pyramid matching scheme based on multi-scale census transform （MSCT）. To localize the BSK in the ROI, cen- sus transform （CT） on gradient images is developed in the second stage. Then gradient encoding histogram （GEH） features and linear support vector machines （SVMs） are used to generate a BSK localization classifier. In the last stage, a condition classifier is trained by SVM, but the features are extracted from gray images. Finally, the ROI, BSK localization, and condition classifiers are cascaded to realize a completely automated inspection system. Experimental results show that the system achieves a correct inspection rate of 99.2% and a speed of 5 frames/s, which represents a good real-time performance and high recognition accuracy.